Convolutional Neural Network Method Research Articles

Face recognition for smart door security access with convolutional neural network method

Face recognition for smart door security access with convolutional neural network method

Classification of nutmeg ripeness using artificial intelligence

Nutmeg seeds can produce a lot of oil if they have optimal maturity, in other words, they have little moisture content. Based on observations I made at one of the refineries in Sukabumi, farmers do not pay attention to the maturity level of nutmeg seeds after drying which can cause a decrease in the quality of nutmeg seeds and the quality of the oil produced. This study aims to make it easier for nutmeg farmers to classify the maturity of nutmeg seeds. This study used the Convolutional Neural Network (CNN) method to help with classification problems and several image processing methods. This program will be run through an android application. The results of CNN model training accuracy are 97.92%. Thus, it can be concluded that the design and testing of a model to classify the maturity level of nutmeg seeds using artificial intelligence and the implementation of the model into an android application has been successfully carried out.

Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network

Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.

Image Classification of Room Tidiness Using VGGNet with Data Augmentation

Tidiness becomes an essential aspect that everyone should maintain. Tidiness encompasses various elements, and one of the aspects closely related to it is the tidiness of a room. The tidiness of a room creates a comfortable and clean environment. The tidiness of a room is particularly crucial for individuals involved in businesses such as the hospitality industry. Therefore, a solution is needed to address this issue, and one of the approaches is to utilize Deep Learning for automatic room tidiness classification. One popular deep learning method for implementing image classification of room tidiness is the convolutional neural network (CNN), which creates a well-performing model for image classification with data augmentation. This research aims to develop an image classification model using CNN with the VGGNet architecture and data augmentation. This study is a reference for further development, with potential applications in the hospitality industry. The research results in a model that achieves an accuracy of 98.44% with a data proportion of 90% for training and validation, while the remaining 10% is used for testing purposes. The conclusion drawn from this study is that the CNN method, combined with data augmentation, can be utilized for image classification of room tidiness.

Seeing is believing: Forecasting crude oil price trend from the perspective of images

AbstractIn this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image‐based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low‐frequency models for high‐frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends.

DATFNets-dynamic adaptive assigned transformer network for fire detection

AbstractFires cause severe damage to the ecological environment and threaten human life and property. Although the traditional convolutional neural network method effectively detects large-area fires, it cannot capture small fires in complex areas through a limited receptive field. At the same time, fires can change at any time due to the influence of wind direction, which challenges fire prevention and control personnel. To solve these problems, a novel dynamic adaptive distribution transformer detection framework is proposed to help firefighters and researchers develop optimal fire management strategies. On the one hand, this framework embeds a context aggregation layer with a masking strategy in the feature extractor to improve the representation of low-level and salient features. The masking strategy can reduce irrelevant information and improve network generalization. On the other hand, designed a dynamic adaptive direction conversion function and sample allocation strategy to fully use adaptive point representation while achieving accurate positioning and classification of fires and screening out representative fire samples in complex backgrounds. In addition, to prevent the network from being limited to the local optimum and discrete points in the sample from causing severe interference to the overall performance, designed a weighted loss function with spatial constraints to optimize the network and penalize the discrete points in the sample. The mAP in the three baseline data sets of FireDets, WildFurgFires, and FireAndSmokes are 0.871, 0.909, and 0.955, respectively. The experimental results are significantly better than other detection methods, which proves that the proposed method has good robustness and detection performance.

Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model

Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies.

Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga

Mango is a fruit known as the "King of Fruit" due to its rich flavor, vast variability, and high nutritional value. Classifying mangoes based on their external appearance is the initial step in the process of identifying and categorizing mango types conventionally. The classification process can be performed by examining external features such as fruit color, shape, and size. Classifying different types of mango fruits accurately can assist researchers in developing superior varieties and also aid farmers for cultivation purposes, sales, distribution, and selecting the right varieties for local growth and weather conditions. This research conducts the classification of mango types based on color from mango images using machine learning. The study compares three methods, namely Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), to determine the best method for classifying mango types based on their images. The dataset underwent preprocessing, where image sizes were standardized to 300 x 300 pixels, and color was changed to grayscale. The dataset was then divided into training and testing data with a ratio of 70:30. Subsequently, the dataset was processed using three methods, and their accuracy results were compared. The findings indicate that the Random Forest method yielded the highest accuracy compared to the other methods, with an accuracy rate of 96%. The accuracy of the SVM method was 95%, and the accuracy of the CNN method was 33%. From these results, it can be concluded that the Random Forest method is highly effective for classifying mango types based on their image compared to SVM and CNN methods.

MA-XRF datasets analysis based on convolutional neural network: A case study on religious panel paintings

Macroscopic X-ray fluorescence (MA-XRF) datasets are analyzed using Artificial Neural Networks. Specifically, Convolutional Neural Networks (CNNs) are trained by coupling the spectra acquired during the MA-XRF scan of two religious panel paintings (“icons”) with the associated Ground-Truth counts per characteristic transition line, as they are extracted by X-ray fluorescence fundamental parameters analysis. In total, twenty thousand XRF spectra were used for the CNN training. The trained neural networks were applied to analyze millions of MA-XRF spectra acquired during the scan of religious painting panels by computing the counts per pixel of X-ray characteristic transition lines and creating the elemental transition maps. Comparison of the CNN extracted results to the Ground-Truth (GT) shows remarkable agreement. The successful MA-XRF datasets analysis applying the CNN method paves an analytical path to the direction of the auto-identification of spectral lines, offering the means for the non-experienced XRF analyst to provide a state-of-the-art analysis and supporting the experienced user not to overlook hardly resolved transition lines.

A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals

Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes a convolutional neural network authentication method based on 2D PPG signals applied to wearable devices. This method uses Markov Transition Field technology to convert one-dimensional PPG signal data into two-dimensional image data, which not only retains the characteristics of the signal but also enriches the spatial information. Afterward, considering that wearable devices usually have limited resources, a lightweight convolutional neural network model is also designed in this method, which reduces resource consumption and computational complexity while ensuring high performance. It is proved experimentally that this method achieves 98.62% and 96.17% accuracy on the training set and test set, respectively, an undeniable advantage compared to the traditional one-dimensional deep learning method and the classical two-dimensional deep learning method.

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

Lung Cancer Detection Using a Modified Convolutional Neural Network (CNN)

<p><span>Image processing is used to classify lung images with malignant or normal nodules. The Convolutional Neural Network (CNN) method is often used to classify images. This study uses a modified CNN architecture with various layers, filters, batch size, dropout, and epoch values. Variations were made to determine the best accuracy value and reduce the overfitting value of the proposed CNN architecture. This study implements the method using the Keras library with the Python programming language. The data is in the form of CT-Scan images of lung cancer and normal lungs. The results of several experiments from the proposed model produce an accuracy value of 95% using three layers, 128 filters on the first layer, 256 on the second layer, and 512 filters on the third layer, then with 32 batch sizes, 0.5 dropout.</span></p>

Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.

Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS. Our retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4-fold validation. The cohort included 35 patients with a mean age of 64 years, and the mean follow-up period was 34 months (2-66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis-free survival, the accuracy was 84.2% with the AUC of 0.852. AI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS.

Similarity analysis of chloride transport behavior in fly ash concrete under different environments aiding by machine learning method

To forecast chloride transport in site by applying accelerated test results, it is necessary to investigate the similarity of chloride diffusivity under different environments. However, limited by test period and cost expenses, a large amount of tests in field and simulated environment are hard to conduct, and a limit test data will make prediction on chloride diffusivity of concrete less precise. To solve this problem, machine learning methods are explored to predict chloride transport. Based on the existing chloride concentration test data of fly ash concrete in natural and accelerated simulated environment, this paper establishes a chloride concentration predictive model based on the one-dimensional convolutional neural network (1D-CNN) method at first, and verifies the stability and accuracy of the established model. Second, similarity models for concentration ratio and diffusion time ratio based on multivariate nonlinear model and based on theoretical Fick’s second law are constructed, and the model accuracy is also tested. Finally, the chloride concentration data predicted by the 1D-CNN model is applied to the constructed similarity models respectively, and the application results are compared and analyzed to determine the optimal similarity model. Results indicate that the concentration predicted by the 1D-CNN method has a strong robustness and a small prediction error. Besides, based on multivariate nonlinear model, the error of concentration predicted by the similarity models for concentration ratio and diffusion time ratio is less than 16% and 30%, respectively. Similarly, according to the theoretical model based on Fick’s second law, the error of concentration predicted by the similarity models for concentration ratio based on the time dependent chloride diffusion coefficient and time dependent peak chloride concentration is less than 21% and 36%, respectively. The similarity model for concentration ratio based on multivariate nonlinear model has the best prediction effect.

PVSPE: A pyramid vision multitask transformer network for spacecraft pose estimation

Spacecraft pose estimation (SPE) plays a vital role in the relative navigation system for on-orbit servicing and active debris removal. Current deep learning-based methods have made great achievements on object pose estimation. However, towards the challenging onboard SPE missions, most existing Convolutional Neural Network (CNN) methods failed to capture remote vision attention, leading to the reduction of accuracy and robustness. In this paper, we presented an end-to-end multi-task Pyramid Transformer SPE network (PVSPE) consisting of two novel feature extraction modules: EnhancedPVT (EnPVT) and SlimGFPN. The EnPVT module is designed to combine global spatial and channel attention, while the Slim GFPN module can fuse features more effectively. Matrix Fisher and multivariate Gaussian distributions are further employed to model the uncertainty of pose regression to increase its accuracy. Extensive experiments are carried out on challenging SPEED + and SHIRT datasets, to validate the performances on pose estimation and vision-based navigation, respectively. The results show that the proposed PVSPE model achieved high accuracy for SPE on the SPEED + dataset even under different scales and severe illumination, demonstrating its robustness and high generalization. Leveraging the insightful uncertainty model of PVSPE, the vision-based navigation pipeline, combined with Kalman filters, accurately estimated the satellite pose under challenging rendezvous scenarios on the SHIRT dataset, with degree-level attitude errors and centimeter-level translation accuracy at steady-state.

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

This research aims to develop a monitoring system and temperature prediction model in neonatal premature infant incubators by applying the internet of things (IoT) concept and the 1-dimensional convolutional neural network (1D-CNN) method. The system is designed by integrating sensors, actuators, and microcontrollers connected through Wi-Fi network with message queue telemetry transport (MQTT) protocol. Sensor data in the incubator is stored in a database and displayed in real-time on a web application. The data in the database is also used for creating a temperature prediction model in the incubator. Test results indicate that the best model configuration consists of 5 neurons in the first layer, 20 neurons in the second layer, and a dense layer with 100. The evaluation of this model yields a high level of accuracy with an root mean square error (RMSE) of 0.200 °C, MSE of 0.004 °C, mean absolute error (MAE) of 0.152 °C, and mean absolute percentage error (MAPE) of 0.4%. Based on the error values obtained between the predicted and actual values from each evaluation technique in the model, it can be concluded that the range between the real and predicted values is approximately 0.2 °C. Overall, this research contributes to improving the quality of care for premature infants.

Implementation of Convolutional Neural Network (CNN) Method for Fish Processed Cuisine Image Identification Application with Google Maps Features

The rapid advancement of science and technology encourages dynamic transformation in various sectors, especially in the field of information and communication technology (ICT), especially with the existence of Android-based smartphones. This advancement revolutionizes the way we access information, especially about various processed fish dishes in Indonesia. However, despite the plethora of culinary offerings, travelers often find it difficult to discover traditional dishes through social media platforms. To bridge this gap, a new app that utilizes artificial neural networks has been developed. The app allows users to photograph and upload images of processed fish dishes to recognize and provide detailed descriptions and recipes. In addition, integrating Google Maps makes it easy for users to find nearby places that serve these dishes. Testing the app with a dataset consisting of 1577 images of six types of processed fish dishes yielded promising results, with accuracy reaching 97.57% over 120 epochs. This innovation not only preserves cultural heritage but also enhances the culinary experience for locals and tourists.

Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science

With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impact in material science studies. In recent years, there has been an increased interest in the use of deep learning in materials science, which has led to significant progress in both fundamental and applied research. One of the most notable advancements is the development of graph convolutional neural network models, which combine graph neural networks and convolutional neural networks to achieve outstanding results in materials science and bridge effectively the deep learning models and material properties predictions. The availability of large materials databases due to the rise of big data has further enhanced the relevance of these models in the field. We present, in this article, a comprehensive overview of graph convolutional neural network models, explaining their fundamental principles and highlighting a few examples of their applications in materials science, as well as current trends. The limitations and challenges that these models face, as well as the potential for future research in this dynamic area are also discussed.

A classification framework with multi-level fusion of object-based analysis and convolutional neural network: a case study for land use classification in mining areas

ABSTRACT To enhance the accuracy of land use classification in mining areas, the Object-based Convolutional Neural Network (OCNN) method has been widely used. However, existing researches tend to neglect the importance of decision-level fusion, focusing only on feature-level fusion. This study proposes a new classification framework with Multi-level Fusion of Object-based analysis and CNN (MFOCNN) to achieves high-accuracy land use classification in mining areas. First, Simple Linear Iterative Cluster (SLIC) is employed to generate image objects, which serve as the basic unit for classification. Second, an improved DenseNet is proposed to extract deep features from image patches, which represent image objects, and provide the classification result. Third, handcrafted features including spectral, textural and geometric of the image objects are extracted and fused with the deep features to obtain the classification result with random forest classifier. Finally, the Dempster-Shafer (DS) evidence theory is applied to fuse the two previously described classification results at the decision-level to obtain the final result. Experiments conducted in the mining areas of Erdos using Gaofen-6 images demonstrate that the proposed MFOCNN achieves the best visual performance and accuracy among all tested methods. The MFOCNN, with its feature-level fusion and decision-level fusion, significantly improves the accuracy of land use classification in mining areas. The results suggest that the proposed MFOCNN is a promising method for achieving high-accuracy land use classification in mining areas.

Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks

Aircraft wake vortices are serious threats to aviation safety. The Pulsed Coherent Doppler Lidar (PCDL) has been widely used in the observation of aircraft wake vortices due to its advantages of high spatial-temporal resolution and high precision. However, the post-processing algorithms require significant computing resources, which cannot achieve the real-time detection of a wake vortex (WV). This paper presents an improved Convolutional Neural Network (CNN) method for WV locating and grading based on PCDL data to avoid the influence of unstable ambient wind fields on the localization and classification results of WV. Typical WV cases are selected for analysis, and the WV locating and grading models are validated on different test sets. The consistency of the analytical algorithm and the CNN algorithm is verified. The results indicate that the improved CNN method achieves satisfactory recognition accuracy with higher efficiency and better robustness, especially in the case of strong turbulence, where the CNN method recognizes the wake vortex while the analytical method cannot. The improved CNN method is expected to be applied to optimize the current aircraft spacing criteria, which is promising in terms of aviation safety and economic benefit improvement.